Multidimensional KNN algorithm based on EEMD and complexity measures in financial time series forecasting

Lin, Guancen; Lin, Aijing; Cao, Jianing

doi:10.1016/j.eswa.2020.114443

Cited by 73 publications

(20 citation statements)

References 46 publications

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“…Author(s) ANN [47], [52], [53], [55], [66], [72], [78], [89], [91] LSTM [31], [60], [67], [80], [92] Decision Tree [25], [79] SVR [71], [74] LRW [61] RVFL [86] ANFIS [63] NFS [82] EGARCH-BPNN [54] FNT [35] FNN [38] KNN [59] Random Forest [95]…”

Section: Table IV Homogeneous Base Learners For Regression Base Learn...mentioning

confidence: 99%

“…Stock price [23], [24], [25], [35], [36], [38], [45], [47], [51], [55], [53], [63], [65], [66], [69], [70], [72], [79], [80], [81], [83], [87], [89], [90], [91], [95] IMFs value [26], [31], [59], [60], [59], [60], [67], [74], [76], [86], [92] Technical indicator value [37], [71] Stock return [52], [82] Stock volatility [54], [50] Effect of external factors [50], [51], [78] Interval of time series [61] V. DECISION FUSION METHODS Admittedly, a better prediction can be obtained by fusing multiple forecasts of the base learners. However, the choice of the fusion method is also critical to the performance of the entire model.…”

Section: Forecasts Of Base Learnersmentioning

confidence: 99%

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Decision Fusion for Stock Market Prediction: A Systematic Review

2022

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Section: Table IV Homogeneous Base Learners For Regression Base Learn...mentioning

confidence: 99%

Section: Forecasts Of Base Learnersmentioning

confidence: 99%

Decision Fusion for Stock Market Prediction: A Systematic Review

2022

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“…Moreover, even the similarity of linear trends is not guaranteed for multidimensional chaos. In the theory of machine observation, such computational schemes are placed in the category of weak classifiers [24][25][26][27].…”

Section: Analog Search In Multidimensional Chaotic Processesmentioning

confidence: 99%

Forecasting Multivariate Chaotic Processes with Precedent Analysis

2021

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Predicting the state of a dynamic system influenced by a chaotic immersion environment is an extremely difficult task, in which the direct use of statistical extrapolation computational schemes is infeasible. This paper considers a version of precedent forecasting in which we use the aftereffects of retrospective observation segments that are similar to the current situation as a forecast. Furthermore, we employ the presence of relatively stable correlations between the parameters of the immersion environment as a regularizing factor. We pay special attention to the choice of similarity measures or distances used to find analog windows in arrays of retrospective multidimensional observations.

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“…With the increasing application of data preprocessing theories such as wavelet transform, empirical mode decomposition (EMD) [9,10], ensemble empirical mode decomposition (EEMD) [11], and variational mode decomposition (VMD) [12,13], in view of the nonlinear and non-stationary characteristics of the load sequence, the data preprocessing method is used to decompose the original sequence, and each sub-sequence is predicted separately, and the prediction result is obtained by superimposing and reconstructing. With the progress of data preprocessing methods, bus load forecasting has more processing methods, and the combined forecasting method has been developed.…”

Section: Introductionmentioning

confidence: 99%

Bus Load Forecasting Method of Power System Based on VMD and Bi-LSTM

et al. 2021

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The effective prediction of bus load can provide an important basis for power system dispatching and planning and energy consumption to promote environmental sustainable development. A bus load forecasting method based on variational modal decomposition (VMD) and bidirectional long short-term memory (Bi-LSTM) network was proposed in this article. Firstly, the bus load series was decomposed into a group of relatively stable subsequence components by VMD to reduce the interaction between different trend information. Then, a time series prediction model based on Bi-LSTM was constructed for each sub sequence, and Bayesian theory was used to optimize the sub sequence-related hyperparameters and judge whether the sequence uses Bi-LSTM to improve the prediction accuracy of a single model. Finally, the bus load prediction value was obtained by superimposing the prediction results of each subsequence. The example results show that compared with the traditional prediction algorithm, the proposed method can better track the change trend of bus load, and has higher prediction accuracy and stability.

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Multidimensional KNN algorithm based on EEMD and complexity measures in financial time series forecasting

Cited by 73 publications

References 46 publications

Decision Fusion for Stock Market Prediction: A Systematic Review

Decision Fusion for Stock Market Prediction: A Systematic Review

Forecasting Multivariate Chaotic Processes with Precedent Analysis

Bus Load Forecasting Method of Power System Based on VMD and Bi-LSTM

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